Evolving Wavelet Networks for Power Transformer Condition Monitoring

This paper proposes a novel model for power transformer condition monitoring using evolving wavelet networks (EWNs). The EWNs are three-layer structures, which contain wavelet, weighting, and summing layers. The EWNs automatically adjust the network parameters, translation, and dilation in the wavelet nodes and the weighting values in the weighting nodes, through an evolutionary-based optimization process. Global search abilities of the evolutionary algorithm as well as the multiresolution and localization natures of the wavelets enable the EWNs to identify the complicated, numerical-knowledge relations of dissolved gas contents in transformer oil to corresponding fault types. The proposed EWNs have been tested on the Taipower Company diagnostic records and compared with the fuzzy diagnosis system, artificial neural networks as well as the conventional method. The test results reveal that the EWNs possess far superior diagnosis accuracy and require less constructing time than the existing methods.